서포트 벡터 머신 과제

credit<-read.csv("credit.csv", header=TRUE, stringsAsFactors = TRUE)
head(credit)
#credit 구조 확인
str(credit)

.

# 훈련용 검증용 분할
library(caret)
set.seed(1000)
inTrain <- createDataPartition( y = credit$default,
                                
                                p = .7,
                                
                                list = FALSE)

#자료구조화인
str(inTrain)

#트레이닝 셋과 테스팅 셋 설정
training <- credit[ inTrain,]
testing <- credit[-inTrain,]

# 행 열 확인
nrow(training)
nrow(testing)


#svm
install.packages("kernlab")
library(kernlab)
data(iris)
svm.kernlab <- ksvm(default ~ ., data = training, type = "C-bsvc",
                      kernel = "rbfdot", kpar = list(sigma = 0.1),
                      C = 10, prob.model = TRUE)
svm.kernlab

fit <- fitted(svm.kernlab)
par(mfrow=c(2,2))
plot(fit, training[,5], main="amount")
plot(fit, training[,10], main="age")
plot(fit, training[,12], main="housing")
plot(fit, training[,16], main="phone")
par(mfrow=c(1,1))



# type= “probabilities”, “decision”, “response”, “votes”


head(predict(svm.kernlab, training, type= "probabilities")) #예측확률

head(predict(svm.kernlab, training, type = "decision")) #편차


# 3개의 이진분류기의 decision value
table(predict(svm.kernlab, testing), testing[,17])


confusionMatrix(predict(svm.kernlab, testing),testing$default)
 
str(credit)

 

20170744권다정 svm과제.R

0.00MB

20170744 권다정 서포트벡터머신.hwp

0.21MB